Protostar Labs

ProtoSDK

ProtoSDK is a product that enables users to deploy AI models to FPGAs by utilizing a streamlined pipeline from model quantization to inference on FPGAs. The pipeline offers a no-code configuration of selected AI models and automatic deployment to selected FPGA boards.

How it Works?

ProtoSDK simplifies AI model deployment to FPGAs through a streamlined, no-code pipeline that takes your model from quantization to inference seamlessly. The process begins with model training, where use-case-specific models are created using supported architectures. Once trained, the models undergo quantization to optimize weights, reducing memory footprint and improving efficiency.

The optimized model is then compiled into an FPGA-compatible format, ensuring smooth integration with the hardware. Finally, the FPGA-compatible model is automatically deployed to the selected board and prepared for inference. With ProtoSDK, deploying AI models to FPGAs is effortless, efficient, and ready for production use.

Product Features

 

ProtoSDK revolutionizes the deployment of AI models to FPGAs with its robust feature set. It provides a no-code solution, enabling users to configure selected AI models effortlessly without programming knowledge. The end-to-end pipeline ensures a seamless workflow from model quantization to inference on FPGAs. Users benefit from a growing list of supported models and boards, ensuring broad compatibility and flexibility.

No-code solution

Fully configurable deployment process, no expert knowledge of FPGA needed

End-to-end pipeline

Start from scratch by training a model, or port an existing model to FPGA board

Growing list of supported models and boards

Continuously updated list of supported devices and models

Automated model deployment

Automatically converts and optimizes AI models for efficient performance on selected FPGA boards

Benefits

No expert knowledge required

Ease of use which enables a wide audience of users to benefit from FPGAs

Benefit from lower power consumption, faster inference and low latency

Models are optimized and ready for integration into production environments

Reduced setup time and simplified process from development to deployment

ProtoSDK Was Created For...

Mission Owners or Satellite Data Providers

System Integrators or Satellite Manufacturers

Payload and Sensor Developers

Mission and Satellite Operators

Tested in Space!

ProtoSDK was successfully deployed on a Intel Cyclone V FPGA (Field Programmable Gate Array) on-board the ESA OPS-SAT Satellite. OPS-SAT was intended for use as a flying laboratory, carrying various instruments and onboard computing resources, namely the FPGA board. The intention was to enable various experiments in orbit for diverse use cases and to achieve first-flight heritage. AnomalAIzer and the ProtoSDK were one of those successful experiments

Deep Layer Library

The deep layer library consists of various neural network layers, complex building blocks, and operations implemented as IP cores, ready to be deployed on FPGAs. That combination is created using standard neural network libraries available in Python supported by IP cores written in VHDL or HLS. It is of key importance to properly connect these two processes so that the FPGA performance requirements are met.

The standard neural network layers are similar to their FPGA quantized counterparts and serve the same purpose. Fully connected or dense layers in this Deep Layer Library are implemented with FPGA combinatorial logic in mind, to achieve the best performance and minimum latency. As for the Convolutional layer, it is ported in such a way to optimally utilize the digital signal processing elements which are limited on the FPGA. This allows us to combine powerful base layers and create complex building blocks of newer models.

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